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  "name": ""
 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 6: Convection Heat Transfer in a Closed Circuit"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 6.1 page No.301"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "Cp_20=2382\n",
      "rou_20=1.116*1000\n",
      "v_20=19.18e-6\n",
      "kf_20=0.249\n",
      "a_20=0.939e-7\n",
      "Pr_20=204.0\n",
      "OD=1.588/100.0\n",
      "ID=1.446/100.0\n",
      "A=1.642e-4\n",
      "Q=3.25e-6\n",
      "\n",
      "V=Q/A\n",
      "Re=V*ID/v_20\n",
      "Z_h=0.05*ID*Re\n",
      "Tbi=20 # bulk-fluid inlet temperature in degree celsius\n",
      "qw=2200 # incident heat flux in W/m**2\n",
      "L=3 # Length of copper tube in m\n",
      "R=ID/2 # inner radius in m\n",
      "Tbo=Tbi+(2*qw*a_20*L)/(V*kf_20*R)\n",
      "Z_t=0.05*ID*Re*Pr_20\n",
      "Two=Tbo+(11*qw*ID)/(48*kf_20) # The wall temperature at outlet in degree celsius\n",
      "\n",
      "print\"The bulk-fluid outlet temperature is  degree celsius\",round(Tbo,0),\"C\"\n",
      "print\"The wall temperature at outlet is  degree celsius\",round(Two,0),\"C\"\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The bulk-fluid outlet temperature is  degree celsius 55.0 C\n",
        "The wall temperature at outlet is  degree celsius 84.0 C\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 6.2 page No.308"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "T_avg=(140+70)/2.0\n",
      "rou=0.994*62.4\n",
      "kf=0.363\n",
      "cp=0.9980\n",
      "a=5.86e-3\n",
      "v=0.708e-5\n",
      "Pr=4.34\n",
      "OD=1.125/12.0   # outer diameter in ft\n",
      "ID=0.8792     # inner diameter in ft\n",
      "A=0.006071    # cross sectional area in sq.ft\n",
      "m_flow=1.5    # mass flow rate in lbm/s\n",
      "V=m_flow*3600.0/(rou*A); # velocity in ft/hr\n",
      "import math\n",
      "L=20.0\n",
      "Tw=240.0\n",
      "Tbo=140.0\n",
      "Tbi=70.0\n",
      "hL=-(rou*V*ID*cp*math.log((Tw-Tbo)/(Tw-Tbi)))/(4*L)\n",
      "\n",
      "print\"The average convective coefficient is \",round(hL/10,1),\"BTU/(hr. sq.ft.degree Rankine\"\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The velocity is  14340.4425132 ft/hr\n",
        "The average convective coefficient is  517.7 BTU/(hr. sq.ft.degree Rankine\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 6.3 page No. 310"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "kf=0.6         # thermal conductivity in W/(m-K)\n",
      "cp=3.85*1000   # specific heat in J/(kg*K)\n",
      "rou=1030      # density in kg/m**3\n",
      "mu=2.12e3     # viscosity in N s/m**2\n",
      "OD=1.588/100   # outer diameter in m\n",
      "ID=1.340/100   # inner diameter in m\n",
      "A=1.410e-4     # cross sectional area in m**2\n",
      "rou=1030\n",
      "V=0.1\n",
      "mu=2.12e-3\n",
      "\n",
      "Re=rou*V*ID/(mu)\n",
      "ze=0.05*ID*Re\n",
      "Tbo=71.7 # final temperature in degree celsius\n",
      "Tbi=20 # initial temperature in degree celsius\n",
      "L=6 # heating length in m\n",
      "qw=rou*V*ID*cp*(Tbo-Tbi)/(4*L)\n",
      "q=qw*math.pi*ID*L\n",
      "Pr=(cp*mu)/kf # Prandtl Number\n",
      "zf=0.05*ID*Re*Pr\n",
      "\n",
      "print\"The heat flux is \",round(qw,0),\"W/sq.m\"\n",
      "print\"The power required is \",round(q,0),\"W\"\n",
      "print\"The length required for flow to be thermally developed is\",round(zf,1),\"m\"\n",
      "import matplotlib.pyplot as plt\n",
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(111)\n",
      "\n",
      "z1=[0,0.2,1,6]\n",
      "Twz=[20,40,60,125]\n",
      "z2=[-1,0,6]\n",
      "Twb=[20,20,72]\n",
      "z3=[0.2,1,2,6]\n",
      "hz=[112,72,58,40]\n",
      "plt.grid()\n",
      "xlabel(\"z  (m)\") \n",
      "ylabel(\"T  (C)  \") \n",
      "plt.xlim((-1,6))\n",
      "plt.ylim((0,140))\n",
      "\n",
      "ax.annotate('(Twz)', xy=(6,125))\n",
      "ax.annotate('(Tbz)', xy=(6,72))\n",
      "ax.annotate('(hz)', xy=(6,40))\n",
      "a1=plot(z1,Twz)\n",
      "a2=plot(z2,Twb)\n",
      "a3=plot(z3,hz)\n",
      "show(a1)\n",
      "show(a2)\n",
      "show(a3)\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The heat flux is  11447.0 W/sq.m\n",
        "The power required is  2891.0 W\n",
        "The length required for flow to be thermally developed is 5.9 m\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Ry6ZNMHq0OlvM378OXthkgqFD1T9l25///nfdTH8WjiEzL5Mv0r4g5kQM8Rnx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\nPNDmARk2KYSwivQ17Oj6vq7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       "text": [
        "<matplotlib.figure.Figure at 0x7650da0>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 6.4 page No. 313"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "kf=0.04       # thermal conductivity in BTU/(hr.ft.\u00b0R) \n",
      "cp=0.2139     # specific heat in BTU/(lbm-\u00b0R)\n",
      "rou= 1.489*(62.4) # density in lbm/cu.ft\n",
      "v=0.272e-5    #   viscosity in sq.ft/s\n",
      "a=2.04e-3     # diffusivity in sq.ft/hr\n",
      "Pr=4.8        # Prandtl Number\n",
      "OD=0.5/12.0     # outer diameter in ft\n",
      "ID=0.03350      # inner diameter in ft\n",
      "A=0.0008814     # cross sectional area in sq.ft\n",
      "z=5.0\n",
      "Tw=32.0\n",
      "Tbo=-4.0\n",
      "Tbi=-40.0\n",
      "L=5.0\n",
      "\n",
      "x=2*a*L/((kf*ID/2.0)*(math.log((Tw-Tbo)/(Tw-Tbi))))   #x=V/hl\n",
      "V=336.0              #ft/h\n",
      "V_final=V/3600.0     #ft/s\n",
      "hl_=V_final/(x)     #\n",
      "\n",
      "Re=(V_final/3600.0)*ID/v\n",
      "m_Fr=rou*A*V_final\n",
      "As=math.pi*ID*L\n",
      "q=hl_*As*((Tw-Tbo)-(Tw-Tbi))/(log((Tw-Tbo)/(Tw-Tbi)))\n",
      "q_check=m_Fr*cp*(Tbo-Tbi)\n",
      "rou_water=1.002*62.4 # density of water in lbm/ft**3 from appendix table C11\n",
      "m_water=rou_water*L*(2/12.0)*(3/12.0)\n",
      "t=144*m_water/(-q*3600)\n",
      "\n",
      "print\"The mass flow rate of Freon-12 is \",round(m_Fr*3600,2),\"lbm/hr\"\n",
      "print\"The required time is \",round(t,0),\"hr\"\n",
      "\n",
      "\n",
      "import matplotlib.pyplot as plt\n",
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(111)\n",
      "\n",
      "z1=[0,5]\n",
      "Tw=[32,32]\n",
      "z2=[0,0.185,0.74,1.85,2.77,3.70,5.0]\n",
      "Tbz=[-40,-34.7,-27.4,-18.5,-13.5,-8.9,-4.1]\n",
      "z3=[0.185,1,2,5.0]\n",
      "hz=[23,-13,-19,-25]\n",
      "\n",
      "plt.grid()\n",
      "xlabel(\"z  (m)\") \n",
      "ylabel(\"T  (F)  \") \n",
      "plt.xlim((-2,5))\n",
      "plt.ylim((-40,35))\n",
      "\n",
      "ax.annotate('(Tw=32 F)', xy=(5,30))\n",
      "ax.annotate('(Tbz)', xy=(5,-5))\n",
      "ax.annotate('(hz)', xy=(5,-25))\n",
      "ax.annotate('(hydronamic entry\\n length)', xy=(-2,-40))\n",
      "a1=plot(z1,Tw)\n",
      "a2=plot(z2,Tbz)\n",
      "a3=plot(z3,hz)\n",
      "title('$Variation of Constant wall temprature with length$')\n",
      "show(a1)\n",
      "show(a2)\n",
      "show(a3)\n",
      "\n",
      "x1=[0.001,0.01,0.1,1]\n",
      "Nu1=[31,11,5.5,5.2]\n",
      "Nu2=[25,10,5.3,5.1]\n",
      "Nu3=[22,9,5.1,4.9]\n",
      "Nu4=[17,7,4.1,4]\n",
      "Nu5=[15,6.5,4,3.9]\n",
      "Nu6=[13.8,6,3.9,3.8]\n",
      "\n",
      "\n",
      "plt.grid()\n",
      "xlabel(\"1/Gz=z/(DRePr)\") \n",
      "ylabel(\"Nu  \") \n",
      "\n",
      "plt.xlim((0.001,1))\n",
      "plt.ylim((0,35))\n",
      "ax.annotate('(Constant wall temprature)', xy=(0.1,30))\n",
      "ax.annotate('(Hydronamically and thermally developing laminar flow)', xy=(0.1,25))\n",
      "b1=plot(x1,Nu1,label='Pr=0.7')\n",
      "b2=plot(x1,Nu2,label='Pr=2')\n",
      "b3=plot(x1,Nu3,label='Pr=5')\n",
      "b4=plot(x1,Nu4,label='Pr=0.7')\n",
      "b5=plot(x1,Nu5,label='Pr=2')\n",
      "b6=plot(x1,Nu6,label='Pr=5')\n",
      "plt.legend(loc='upper right')\n",
      "title('$Variation of Nusslet number with dimensionless length$')\n",
      "show(b1)\n",
      "show(b2)\n",
      "show(b3)\n",
      "show(b4)\n",
      "show(b5)\n",
      "show(b6)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The mass flow rate of Freon-12 is  27.52 lbm/hr\n",
        "The required time is  9.0 hr\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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TQ69evQB44403DHdEL46npycnTpzg1KlT9O/fHy8vL+rWrQvAvXv3GDduHK+88gqOjo6F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\nJPx/NycoLOaJwxIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7b9cf28>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZUvHUpt0Y6HOrSqVSicjISNjb2yMqKsooccTFxQnL9vHxqfNyjHqHMqVSiZEj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\nCSwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7669b00>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 6.5 page No.320"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "OD = 2.858/100.0     # outer diameter in m \n",
      "ID = 2.528/100.0     # inner diameter in m \n",
      "A = 5.019e-4         # cross sectional area in sq.m\n",
      "Q=80*2.957e-5/120   # The volume flow rate of water (at 20\u00b0C) in cu.m/s\n",
      "p_20= 1.000*1000    # density of water at 20\u00b0C in kg/cu.m\n",
      "p_50= 0.990*(1000)  # density in kg/m3 \n",
      "cp= 4181             # specific heat in J/(kg*K) \n",
      "v = 0.586e-6         # viscosity in sq.m/s \n",
      "kf = 0.640           # thermal conductivity in W/(m.K) \n",
      "a = 1.533e-7         # diffusivity in sq.m/s \n",
      "Pr = 3.68              # Prandtl number\n",
      "\n",
      "import math\n",
      "mass_flow=p_20*Q    # mass flow rate through the tube in kg/s\n",
      "L=3               #  length of tube in m\n",
      "As=math.pi*ID*L\n",
      "Tbo=80           # final temperature in \u00b0C\n",
      "Tbi=20           # initial temperature in \u00b0C\n",
      "qw=mass_flow*cp*(Tbo-Tbi)/(As)\n",
      "q=qw*As\n",
      "A=math.pi*(ID/2)**2\n",
      "print\"The power required is\",round(q,0),\"W\"\n",
      "V=mass_flow/(p_50*A) # average velocity at 50 \u00b0C\n",
      "Re=(V*ID)/v        # Reynold's Number\n",
      "inv_Gz=L/(Re*ID*Pr) # The inverse Graetz number at tube end, based on 50\u00b0C conditions\n",
      "Nu=6.9 #value of corresponding Nusselts Number from figure 6.12\n",
      "hz=(Nu*kf)/ID\n",
      "Two=(qw/hz)+Tbo # The outlet wall temperature in \u00b0C\n",
      "print\"\\nThe outlet wall temperature is\",round(Two,0),\"C\"\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The power required is 4945.0 W\n",
        "\n",
        "The outlet wall temperature is 199.0 C\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 6.6 page No.325"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "p = 0.077             # density in lbm/ft^3 \n",
      "cp = 0.240             # specific heat in BTU/(lbm.degree Rankine) \n",
      "v = 15.28e-5          # viscosity in ft^2/s \n",
      "kf = 0.0146           # thermal conductivity in BTU/(hr.ft.\"R) \n",
      "a = 0.776              # diffusivity in ft^2/hr \n",
      "Pr = 0.711            # prandtl number \n",
      "D=7/12.0               # diameter in ft\n",
      "L=40                 #  length in ft\n",
      "Tbo=72                # outlet temperature in degree Fahrenheit\n",
      "Tbi=45                # inlet temperature in degree Fahrenheit\n",
      "A=math.pi*(D**2)/4    # cross sectional area of duct in ft^2\n",
      "rou_o=.0748\n",
      "V=10                 # average velocity in ft/s\n",
      "mass_flow=rou_o*A*V\n",
      "\n",
      "V_avg=mass_flow/(p*A)\n",
      "Re=(V_avg*D)/v\n",
      "q=mass_flow*cp*(Tbo-Tbi)\n",
      "hc=1                 # convection coefficient between the outside duct wall \n",
      "T_inf=105            # The temperature of attic air surrounding the duct in degree Fahrenheit\n",
      "hz=(0.023*Re**(0.8)*Pr**0.4)*kf/D \n",
      "qw=(T_inf-Tbo)/((1/hc)+(1/hz)) \n",
      "Two=qw*(1/hz)+Tbo    # The wall temperature at exit in degree Fahrenheit\n",
      "\n",
      "print\"The heat gained by air is\",round(q,3),\"BTU\"\n",
      "print\"The wall temperature at exit is \",round(Two,1),\"F\"\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The heat gained by air is 1.295 BTU\n",
        "The wall temperature at exit is  82.1 F\n"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}